1. Field of Invention
The present invention relates generally to computer recognition of an image and more particularly to optimizing the speed with which the image is recognized in a real scene.
2. Prior Art
As our visual world is becoming richer and our life is becoming full of screens projecting for us visual information our necessity to analyze and understand what we are seeing is becoming even more and more essential. Computers, Mobile devices, Television, Medical Equipments, Video Conferences, Video Screen Glasses are only few of the visual information sources that bring to our eyes information. Therefore, image processing capabilities allow us to recognize images, objects, faces and movements in both reality scenes and all of our visual information sources.
Feature point extraction and recognition is a key to modern approaches to image recognition, face recognition and object detection or movement detection, but these approaches generally require computationally expensive processing times. In other words, they take a long time and present a fundamental problem in computer vision, particularly object recognition, object tracking, and object localization.
In the prior art there are methods for image or object or face recognition and in such prior art systems a correspondence between the object to be recognized in the current scene has to be recognized. The set of correspondences (C) is generally automatically generated using feature detectors which try to identify and map several features of the image on the current frame. Such approaches include scale invariant feature transform (SIFT), speeded up robust features (SURF or Java SURF) and center-surround extremas (CenSurE).
However, such image processing features extraction approaches still require computationally expensive processing times and are still not applicable to mobile devices such as smart phones, entertainment devices such as TV, mobile sophisticated devices for military purposes.
One of the problems in image processing is the time it takes for the processors to carry out the tasks of feature extraction and comparisons and the requirement of heavy computational time. This creates low performance and bad user experience especially in real time responsiveness requirement.
One solution used to increase the speed of the processing is by down scaling both database information and input scene information to work only less quantity of information. This solution brings up acceleration benefits, however, it can lead to accuracy problems since reducing the size means reducing and losing part of the original data and therefore ending up with mismatches and low quality of the recognition which can be very crucial in some cases like medical or military systems and very unpleasant experience when a user has high definition quality images and on the other hand a system that as bigger the information is the worse is the quality of the recognition.